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25
docs/en/sdks/JS-sdk/_index.md
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25
docs/en/sdks/JS-sdk/_index.md
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@@ -0,0 +1,25 @@
|
||||
---
|
||||
title: "JS SDK"
|
||||
type: docs
|
||||
weight: 7
|
||||
description: >
|
||||
JS SDKs to connect to the MCP Toolbox server.
|
||||
---
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
The MCP Toolbox service provides a centralized way to manage and expose tools
|
||||
(like API connectors, database query tools, etc.) for use by GenAI applications.
|
||||
|
||||
These JS SDKs act as clients for that service. They handle the communication needed to:
|
||||
|
||||
* Fetch tool definitions from your running Toolbox instance.
|
||||
* Provide convenient JS objects or functions representing those tools.
|
||||
* Invoke the tools (calling the underlying APIs/services configured in Toolbox).
|
||||
* Handle authentication and parameter binding as needed.
|
||||
|
||||
By using these SDKs, you can easily leverage your Toolbox-managed tools directly
|
||||
within your JS applications or AI orchestration frameworks.
|
||||
|
||||
[Github](https://github.com/googleapis/mcp-toolbox-sdk-js)
|
||||
@@ -1,15 +0,0 @@
|
||||
---
|
||||
title: "Go SDK"
|
||||
weight: 2
|
||||
description: Go lang client SDK
|
||||
icon: fa-brands fa-golang
|
||||
manualLink: "https://github.com/googleapis/mcp-toolbox-sdk-go"
|
||||
manualLinkTarget: _blank
|
||||
---
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<link rel="canonical" href="https://github.com/googleapis/mcp-toolbox-sdk-go"/>
|
||||
<meta http-equiv="refresh" content="0;url=https://github.com/googleapis/mcp-toolbox-sdk-go"/>
|
||||
</head>
|
||||
</html>
|
||||
25
docs/en/sdks/go-sdk/_index.md
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25
docs/en/sdks/go-sdk/_index.md
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@@ -0,0 +1,25 @@
|
||||
---
|
||||
title: "Go SDK"
|
||||
type: docs
|
||||
weight: 7
|
||||
description: >
|
||||
Go SDKs to connect to the MCP Toolbox server.
|
||||
---
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
The MCP Toolbox service provides a centralized way to manage and expose tools
|
||||
(like API connectors, database query tools, etc.) for use by GenAI applications.
|
||||
|
||||
The Go SDK act as clients for that service. They handle the communication needed to:
|
||||
|
||||
* Fetch tool definitions from your running Toolbox instance.
|
||||
* Provide convenient Go structs representing those tools.
|
||||
* Invoke the tools (calling the underlying APIs/services configured in Toolbox).
|
||||
* Handle authentication and parameter binding as needed.
|
||||
|
||||
By using the SDK, you can easily leverage your Toolbox-managed tools directly
|
||||
within your Go applications or AI orchestration frameworks.
|
||||
|
||||
[Github](https://github.com/googleapis/mcp-toolbox-sdk-go)
|
||||
@@ -1,15 +0,0 @@
|
||||
---
|
||||
title: "JS SDK"
|
||||
weight: 2
|
||||
description: Javascript client SDK
|
||||
icon: fa-brands fa-node-js
|
||||
manualLink: "https://github.com/googleapis/mcp-toolbox-sdk-js"
|
||||
manualLinkTarget: _blank
|
||||
---
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<link rel="canonical" href="https://github.com/googleapis/mcp-toolbox-sdk-js"/>
|
||||
<meta http-equiv="refresh" content="0;url=https://github.com/googleapis/mcp-toolbox-sdk-js"/>
|
||||
</head>
|
||||
</html>
|
||||
@@ -1,15 +0,0 @@
|
||||
---
|
||||
title: "Python SDK"
|
||||
weight: 2
|
||||
description: Python client SDK
|
||||
icon: fa-brands fa-python
|
||||
manualLink: "https://github.com/googleapis/mcp-toolbox-sdk-python"
|
||||
manualLinkTarget: _blank
|
||||
---
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<link rel="canonical" href="https://github.com/googleapis/mcp-toolbox-sdk-python"/>
|
||||
<meta http-equiv="refresh" content="0;url=https://github.com/googleapis/mcp-toolbox-sdk-python"/>
|
||||
</head>
|
||||
</html>
|
||||
55
docs/en/sdks/python-sdk/_index.md
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55
docs/en/sdks/python-sdk/_index.md
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@@ -0,0 +1,55 @@
|
||||
---
|
||||
title: "Python SDK"
|
||||
type: docs
|
||||
weight: 7
|
||||
description: >
|
||||
Python SDKs to connect to the MCP Toolbox server.
|
||||
---
|
||||
|
||||
|
||||
## Overview
|
||||
|
||||
The MCP Toolbox service provides a centralized way to manage and expose tools
|
||||
(like API connectors, database query tools, etc.) for use by GenAI applications.
|
||||
|
||||
These Python SDKs act as clients for that service. They handle the communication needed to:
|
||||
|
||||
* Fetch tool definitions from your running Toolbox instance.
|
||||
* Provide convenient Python objects or functions representing those tools.
|
||||
* Invoke the tools (calling the underlying APIs/services configured in Toolbox).
|
||||
* Handle authentication and parameter binding as needed.
|
||||
|
||||
By using these SDKs, you can easily leverage your Toolbox-managed tools directly
|
||||
within your Python applications or AI orchestration frameworks.
|
||||
|
||||
## Which Package Should I Use?
|
||||
|
||||
Choosing the right package depends on how you are building your application:
|
||||
|
||||
* [`toolbox-langchain`](langchain):
|
||||
Use this package if you are building your application using the LangChain or
|
||||
LangGraph frameworks. It provides tools that are directly compatible with the
|
||||
LangChain ecosystem (`BaseTool` interface), simplifying integration.
|
||||
* [`toolbox-llamaindex`](llamaindex):
|
||||
Use this package if you are building your application using the LlamaIndex framework.
|
||||
It provides tools that are directly compatible with the
|
||||
LlamaIndex ecosystem (`BaseTool` interface), simplifying integration.
|
||||
* [`toolbox-core`](core):
|
||||
Use this package if you are not using LangChain/LangGraph or any other
|
||||
orchestration framework, or if you need a framework-agnostic way to interact
|
||||
with Toolbox tools (e.g., for custom orchestration logic or direct use in
|
||||
Python scripts).
|
||||
|
||||
## Available Packages
|
||||
|
||||
This repository hosts the following Python packages. See the package-specific
|
||||
README for detailed installation and usage instructions:
|
||||
|
||||
| Package | Target Use Case | Integration | Path | Details (README) | PyPI Status |
|
||||
| :------ | :---------- | :---------- | :---------------------- | :---------- | :---------
|
||||
| `toolbox-core` | Framework-agnostic / Custom applications | Use directly / Custom | `packages/toolbox-core/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-core/README.md) |  |
|
||||
| `toolbox-langchain` | LangChain / LangGraph applications | LangChain / LangGraph | `packages/toolbox-langchain/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-langchain/README.md) |  |
|
||||
| `toolbox-llamaindex` | LlamaIndex applications | LlamaIndex | `packages/toolbox-llamaindex/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-llamaindex/README.md) |  |
|
||||
|
||||
|
||||
[Github](https://github.com/googleapis/mcp-toolbox-sdk-python)
|
||||
401
docs/en/sdks/python-sdk/langchain/index.md
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401
docs/en/sdks/python-sdk/langchain/index.md
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@@ -0,0 +1,401 @@
|
||||
---
|
||||
title: "Toolbox-langchain"
|
||||
type: docs
|
||||
weight: 8
|
||||
description: >
|
||||
Toolbox-langchain SDK for connecting to the MCP Toolbox server and invoking tools programmatically.
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The `toolbox-langchain` package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install toolbox-langchain
|
||||
```
|
||||
## Quickstart
|
||||
|
||||
Here's a minimal example to get you started using
|
||||
[LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent):
|
||||
|
||||
```py
|
||||
from toolbox_langchain import ToolboxClient
|
||||
from langchain_google_vertexai import ChatVertexAI
|
||||
from langgraph.prebuilt import create_react_agent
|
||||
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||
tools = toolbox.load_toolset()
|
||||
|
||||
model = ChatVertexAI(model="gemini-2.0-flash-001")
|
||||
agent = create_react_agent(model, tools)
|
||||
|
||||
prompt = "How's the weather today?"
|
||||
|
||||
for s in agent.stream({"messages": [("user", prompt)]}, stream_mode="values"):
|
||||
message = s["messages"][-1]
|
||||
if isinstance(message, tuple):
|
||||
print(message)
|
||||
else:
|
||||
message.pretty_print()
|
||||
```
|
||||
{{< notice tip >}}
|
||||
For a complete, end-to-end example including setting up the service and using an SDK, see the full tutorial: [Toolbox Quickstart Tutorial](getting-started/local_quickstart)
|
||||
{{< /notice >}}
|
||||
|
||||
## Usage
|
||||
|
||||
Import and initialize the toolbox client.
|
||||
|
||||
```py
|
||||
from toolbox_langchain import ToolboxClient
|
||||
|
||||
# Replace with your Toolbox service's URL
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||
```
|
||||
|
||||
## Loading Tools
|
||||
|
||||
### Load a toolset
|
||||
|
||||
A toolset is a collection of related tools. You can load all tools in a toolset
|
||||
or a specific one:
|
||||
|
||||
```py
|
||||
# Load all tools
|
||||
tools = toolbox.load_toolset()
|
||||
|
||||
# Load a specific toolset
|
||||
tools = toolbox.load_toolset("my-toolset")
|
||||
```
|
||||
|
||||
### Load a single tool
|
||||
|
||||
```py
|
||||
tool = toolbox.load_tool("my-tool")
|
||||
```
|
||||
|
||||
Loading individual tools gives you finer-grained control over which tools are
|
||||
available to your LLM agent.
|
||||
|
||||
## Use with LangChain
|
||||
|
||||
LangChain's agents can dynamically choose and execute tools based on the user
|
||||
input. Include tools loaded from the Toolbox SDK in the agent's toolkit:
|
||||
|
||||
```py
|
||||
from langchain_google_vertexai import ChatVertexAI
|
||||
|
||||
model = ChatVertexAI(model="gemini-2.0-flash-001")
|
||||
|
||||
# Initialize agent with tools
|
||||
agent = model.bind_tools(tools)
|
||||
|
||||
# Run the agent
|
||||
result = agent.invoke("Do something with the tools")
|
||||
```
|
||||
|
||||
## Use with LangGraph
|
||||
|
||||
Integrate the Toolbox SDK with LangGraph to use Toolbox service tools within a
|
||||
graph-based workflow. Follow the [official
|
||||
guide](https://langchain-ai.github.io/langgraph/) with minimal changes.
|
||||
|
||||
### Represent Tools as Nodes
|
||||
|
||||
Represent each tool as a LangGraph node, encapsulating the tool's execution within the node's functionality:
|
||||
|
||||
```py
|
||||
from toolbox_langchain import ToolboxClient
|
||||
from langgraph.graph import StateGraph, MessagesState
|
||||
from langgraph.prebuilt import ToolNode
|
||||
|
||||
# Define the function that calls the model
|
||||
def call_model(state: MessagesState):
|
||||
messages = state['messages']
|
||||
response = model.invoke(messages)
|
||||
return {"messages": [response]} # Return a list to add to existing messages
|
||||
|
||||
model = ChatVertexAI(model="gemini-2.0-flash-001")
|
||||
builder = StateGraph(MessagesState)
|
||||
tool_node = ToolNode(tools)
|
||||
|
||||
builder.add_node("agent", call_model)
|
||||
builder.add_node("tools", tool_node)
|
||||
```
|
||||
|
||||
### Connect Tools with LLM
|
||||
|
||||
Connect tool nodes with LLM nodes. The LLM decides which tool to use based on
|
||||
input or context. Tool output can be fed back into the LLM:
|
||||
|
||||
```py
|
||||
from typing import Literal
|
||||
from langgraph.graph import END, START
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
# Define the function that determines whether to continue or not
|
||||
def should_continue(state: MessagesState) -> Literal["tools", END]:
|
||||
messages = state['messages']
|
||||
last_message = messages[-1]
|
||||
if last_message.tool_calls:
|
||||
return "tools" # Route to "tools" node if LLM makes a tool call
|
||||
return END # Otherwise, stop
|
||||
|
||||
builder.add_edge(START, "agent")
|
||||
builder.add_conditional_edges("agent", should_continue)
|
||||
builder.add_edge("tools", 'agent')
|
||||
|
||||
graph = builder.compile()
|
||||
|
||||
graph.invoke({"messages": [HumanMessage(content="Do something with the tools")]})
|
||||
```
|
||||
|
||||
## Manual usage
|
||||
|
||||
Execute a tool manually using the `invoke` method:
|
||||
|
||||
```py
|
||||
result = tools[0].invoke({"name": "Alice", "age": 30})
|
||||
```
|
||||
|
||||
This is useful for testing tools or when you need precise control over tool
|
||||
execution outside of an agent framework.
|
||||
|
||||
## Client to Server Authentication
|
||||
|
||||
This section describes how to authenticate the ToolboxClient itself when
|
||||
connecting to a Toolbox server instance that requires authentication. This is
|
||||
crucial for securing your Toolbox server endpoint, especially when deployed on
|
||||
platforms like Cloud Run, GKE, or any environment where unauthenticated access
|
||||
is restricted.
|
||||
|
||||
This client-to-server authentication ensures that the Toolbox server can verify
|
||||
the identity of the client making the request before any tool is loaded or
|
||||
called. It is different from [Authenticating Tools](#authenticating-tools),
|
||||
which deals with providing credentials for specific tools within an already
|
||||
connected Toolbox session.
|
||||
|
||||
### When is Client-to-Server Authentication Needed?
|
||||
|
||||
You'll need this type of authentication if your Toolbox server is configured to
|
||||
deny unauthenticated requests. For example:
|
||||
|
||||
- Your Toolbox server is deployed on Cloud Run and configured to "Require authentication."
|
||||
- Your server is behind an Identity-Aware Proxy (IAP) or a similar
|
||||
authentication layer.
|
||||
- You have custom authentication middleware on your self-hosted Toolbox server.
|
||||
|
||||
Without proper client authentication in these scenarios, attempts to connect or
|
||||
make calls (like `load_tool`) will likely fail with `Unauthorized` errors.
|
||||
|
||||
### How it works
|
||||
|
||||
The `ToolboxClient` allows you to specify functions (or coroutines for the async
|
||||
client) that dynamically generate HTTP headers for every request sent to the
|
||||
Toolbox server. The most common use case is to add an Authorization header with
|
||||
a bearer token (e.g., a Google ID token).
|
||||
|
||||
These header-generating functions are called just before each request, ensuring
|
||||
that fresh credentials or header values can be used.
|
||||
|
||||
### Configuration
|
||||
|
||||
You can configure these dynamic headers as follows:
|
||||
|
||||
```python
|
||||
from toolbox_langchain import ToolboxClient
|
||||
|
||||
async with ToolboxClient(
|
||||
"toolbox-url",
|
||||
client_headers={"header1": header1_getter, "header2": header2_getter, ...}
|
||||
) as client:
|
||||
```
|
||||
|
||||
### Authenticating with Google Cloud Servers
|
||||
|
||||
For Toolbox servers hosted on Google Cloud (e.g., Cloud Run) and requiring
|
||||
`Google ID token` authentication, the helper module
|
||||
[auth_methods](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-core/src/toolbox_core/auth_methods.py) provides utility functions.
|
||||
|
||||
### Step by Step Guide for Cloud Run
|
||||
|
||||
1. **Configure Permissions**:
|
||||
[Grant](https://cloud.google.com/run/docs/securing/managing-access#service-add-principals)
|
||||
the `roles/run.invoker` IAM role on the Cloud
|
||||
Run service to the principal. This could be your `user account email` or a
|
||||
`service account`.
|
||||
2. **Configure Credentials**
|
||||
- Local Development: Set up
|
||||
[ADC](https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
|
||||
- Google Cloud Environments: When running within Google Cloud (e.g., Compute
|
||||
Engine, GKE, another Cloud Run service, Cloud Functions), ADC is typically
|
||||
configured automatically, using the environment's default service account.
|
||||
3. **Connect to the Toolbox Server**
|
||||
|
||||
```python
|
||||
from toolbox_langchain import ToolboxClient
|
||||
from toolbox_core import auth_methods
|
||||
|
||||
auth_token_provider = auth_methods.aget_google_id_token(URL) # can also use sync method
|
||||
async with ToolboxClient(
|
||||
URL,
|
||||
client_headers={"Authorization": auth_token_provider},
|
||||
) as client:
|
||||
tools = client.load_toolset()
|
||||
|
||||
# Now, you can use the client as usual.
|
||||
```
|
||||
|
||||
|
||||
## Authenticating Tools
|
||||
|
||||
{{< notice info >}}
|
||||
Always use HTTPS to connect your application with the Toolbox service, especially when using tools with authentication configured. Using HTTP exposes your application to serious security risks.
|
||||
{{< /notice >}}
|
||||
|
||||
Some tools require user authentication to access sensitive data.
|
||||
|
||||
### Supported Authentication Mechanisms
|
||||
Toolbox currently supports authentication using the [OIDC
|
||||
protocol](https://openid.net/specs/openid-connect-core-1_0.html) with [ID
|
||||
tokens](https://openid.net/specs/openid-connect-core-1_0.html#IDToken) (not
|
||||
access tokens) for [Google OAuth
|
||||
2.0](https://cloud.google.com/apigee/docs/api-platform/security/oauth/oauth-home).
|
||||
|
||||
### Configure Tools
|
||||
|
||||
Refer to [these
|
||||
instructions](https://googleapis.github.io/genai-toolbox/resources/tools/#authenticated-parameters) on
|
||||
configuring tools for authenticated parameters.
|
||||
|
||||
### Configure SDK
|
||||
|
||||
You need a method to retrieve an ID token from your authentication service:
|
||||
|
||||
```py
|
||||
async def get_auth_token():
|
||||
# ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
|
||||
# This example just returns a placeholder. Replace with your actual token retrieval.
|
||||
return "YOUR_ID_TOKEN" # Placeholder
|
||||
```
|
||||
|
||||
#### Add Authentication to a Tool
|
||||
|
||||
```py
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||
tools = toolbox.load_toolset()
|
||||
|
||||
auth_tool = tools[0].add_auth_token_getter("my_auth", get_auth_token) # Single token
|
||||
|
||||
multi_auth_tool = tools[0].add_auth_token_getters({"auth_1": get_auth_1}, {"auth_2": get_auth_2}) # Multiple tokens
|
||||
|
||||
# OR
|
||||
|
||||
auth_tools = [tool.add_auth_token_getter("my_auth", get_auth_token) for tool in tools]
|
||||
```
|
||||
|
||||
#### Add Authentication While Loading
|
||||
|
||||
```py
|
||||
auth_tool = toolbox.load_tool(auth_token_getters={"my_auth": get_auth_token})
|
||||
|
||||
auth_tools = toolbox.load_toolset(auth_token_getters={"my_auth": get_auth_token})
|
||||
```
|
||||
{{< notice note >}}
|
||||
Adding auth tokens during loading only affect the tools loaded within that call.
|
||||
{{< /notice >}}
|
||||
|
||||
### Complete Example
|
||||
|
||||
```py
|
||||
import asyncio
|
||||
from toolbox_langchain import ToolboxClient
|
||||
|
||||
async def get_auth_token():
|
||||
# ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
|
||||
# This example just returns a placeholder. Replace with your actual token retrieval.
|
||||
return "YOUR_ID_TOKEN" # Placeholder
|
||||
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||
tool = toolbox.load_tool("my-tool")
|
||||
|
||||
auth_tool = tool.add_auth_token_getter("my_auth", get_auth_token)
|
||||
result = auth_tool.invoke({"input": "some input"})
|
||||
print(result)
|
||||
```
|
||||
|
||||
## Binding Parameter Values
|
||||
|
||||
Predetermine values for tool parameters using the SDK. These values won't be
|
||||
modified by the LLM. This is useful for:
|
||||
|
||||
* **Protecting sensitive information:** API keys, secrets, etc.
|
||||
* **Enforcing consistency:** Ensuring specific values for certain parameters.
|
||||
* **Pre-filling known data:** Providing defaults or context.
|
||||
|
||||
### Binding Parameters to a Tool
|
||||
|
||||
```py
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||
tools = toolbox.load_toolset()
|
||||
|
||||
bound_tool = tool[0].bind_param("param", "value") # Single param
|
||||
|
||||
multi_bound_tool = tools[0].bind_params({"param1": "value1", "param2": "value2"}) # Multiple params
|
||||
|
||||
# OR
|
||||
|
||||
bound_tools = [tool.bind_param("param", "value") for tool in tools]
|
||||
```
|
||||
|
||||
### Binding Parameters While Loading
|
||||
|
||||
```py
|
||||
bound_tool = toolbox.load_tool("my-tool", bound_params={"param": "value"})
|
||||
|
||||
bound_tools = toolbox.load_toolset(bound_params={"param": "value"})
|
||||
```
|
||||
{{< notice note >}}
|
||||
Bound values during loading only affect the tools loaded in that call.
|
||||
{{< /notice >}}
|
||||
|
||||
### Binding Dynamic Values
|
||||
|
||||
Use a function to bind dynamic values:
|
||||
|
||||
```py
|
||||
def get_dynamic_value():
|
||||
# Logic to determine the value
|
||||
return "dynamic_value"
|
||||
|
||||
dynamic_bound_tool = tool.bind_param("param", get_dynamic_value)
|
||||
```
|
||||
{{< notice note >}}
|
||||
You don’t need to modify tool configurations to bind parameter values.
|
||||
{{< /notice >}}
|
||||
|
||||
## Asynchronous Usage
|
||||
|
||||
For better performance through [cooperative
|
||||
multitasking](https://en.wikipedia.org/wiki/Cooperative_multitasking), you can
|
||||
use the asynchronous interfaces of the `ToolboxClient`.
|
||||
|
||||
{{< notice note >}}
|
||||
Asynchronous interfaces like `aload_tool` and `aload_toolset` require an asynchronous environment. For guidance on running asynchronous Python programs, see [asyncio documentation](https://docs.python.org/3/library/asyncio-runner.html#running-an-asyncio-program).
|
||||
{{< /notice >}}
|
||||
|
||||
```py
|
||||
import asyncio
|
||||
from toolbox_langchain import ToolboxClient
|
||||
|
||||
async def main():
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||
tool = await client.aload_tool("my-tool")
|
||||
tools = await client.aload_toolset()
|
||||
response = await tool.ainvoke()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
Reference in New Issue
Block a user